Lucas Lacasa (Queen Mary University of London)
As the number of cases of COVID-19 continues to grow, local health services across different countries are at risk of being overwhelmed with patients requiring intensive care. At the same time, surges and demand are not homogeneous across a country, as different regions see incidence grow or decline in an asynchronous way. This enables the possibility of balancing demand by sharing patients. In this talk I will describe a proposal that we put forward in late March 2020 during the first wave of COVID-19, which computes quasi-optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. The method is general and applicable regionally or at a national level. I will give the details of the method and showcase it with realistic data from the United Kingdom and Spain. Depending on different ICU demand profiles, up to 1000 patients (per algorithm step) which would otherwise not receive care could be re-allocated without the needs of increasing capacity of the hospitals. I will also briefly discuss our experience in going from the scientific idea to the operationalised platform.
Maria del Rio-Chanona (University of Oxford)
We develop a non-equilibrium production network model for predicting the economic impact of the COVID-19 pandemic. In the first part of this work, we made quantitative predictions of first-order supply and demand shocks for the U.S. economy associated with the COVID-19 pandemic at the level of individual occupations and industries. To analyze the supply shock, we classify industries as essential or non-essential and construct a Remote Labor Index, which measures the ability of different occupations to work from home. Demand shocks are based on a study of the likely effect of a severe influenza epidemic developed by the US Congressional Budget Office. Compared to the pre-COVID period, these shocks would threaten around 20% of the US economy’s GDP, jeopardise 23% of jobs and reduce total wage income by 16%. We then design an economic model to address the unique features of the COVID-19 pandemic. Our model also includes a production function that distinguishes between critical and non-critical inputs, inventory dynamics, and feedback between unemployment and consumption. We demonstrate that economic outcomes are very sensitive to the choice of the production function, show how supply constraints cause strong network effects, and find some counter-intuitive effects, such as that reopening only a few industries can actually lower aggregate output. Our results suggest that there may be a reasonable compromise that yields a relatively small increase in R0 and delivers a substantial boost in economic output. This corresponds to a situation in which all non-consumer facing industries reopen, schools are open only for workers who need childcare, and everyone who can work from home continues to work from home.
Aleksandra Walczak (ENS, Paris)
The immune repertoire responds to a wide variety of pathogenic threats. Immune repertoire sequencing experiments give us insight into the composition of these repertoires. Since the functioning of the repertoire relies on statistical properties, statistical analysis is needed to identify responding clones. Using such methods I will describe the repertoire level response to the SARS-CoV-2, among other perturbations. More generally, I will show how immune repertoires provide a unique fingerprint reflecting the immune history of individuals, with potential applications in precision medicine.
Cicle de Webinars sobre Sistemes Complexos enfocats al COVID-19 organitzat per l'UBICS i complexitat.cat. Dirigit a tothom interessat en els Sistemes Complexos i els seus actuals camps d’aplicació.
11 Juny (16h) - Manlio de Domenico, CoMuNe Lab, Fondazione Bruno Kessler
"Tackling complexity: foundations and appplications"
Abstract: Complex systems consists of units whose interactions at a microscopic scale lead to the spontaneous emergence of collective behavior and other unexpected phenomena at the meso- and macroscale. In this seminar I will introduce some basic concepts and tools of complexity science without relying on technicalities. In the second part of the seminar I will briefly discuss the relevance of big data for the analysis of complex systems and, more specifically, of socio-technical systems, spanning from the rise of collective attention to one of the most relevant phenomena observed during the COVID-19 pandemic: the infodemic related to coronavirus.
18 Juny (16h) - Nuria Oliver, Data-Pop Alliance & ELLIS (The European Laboratory for Learning and Intelligent Systems)
"Data Science to fight against COVID-19"
Abstract: In my talk, I will describe the work that we have done within the Commission on AI and COVID-19 for the President of the Valencian Region. As commissioner, I have led a multi-disciplinary team of 20+ scientists who have volunteered since March 2020. We have been working on 4 large areas: (1) human mobility modeling; (2) computational epidemiological models (both metapopulation and individual models); (3) predictive models; (4) citizen surveys: https://covid19impactsurvey.org.
I will describe the results that we have produced in each of these areas and will share the lessons learned in this very special initiative of collaboration between the civil society at large (through the survey), the scientific community (through the Expert Group) and a public administration (through the Commissioner at the Presidency level).
25 Juny (16h) - Santiago F. Elena, Instituto de Biología Integrativa de Sistemas, CSIC
“Identifying early-warning signals for the sudden transition from health to disease stages by dynamical network biomarkers”
Abstract: One of the most outstanding observations during COVID-19 pandemics is that some patients have an asymptomatic infection while others suffer severe symptoms, some of them becoming fatal. Is there any relation between the global gene expression state of patients and they propensity to suffer an asymptomatic infection? Is it possible to identify which genes, or groups of them within a regulation network, may serve as markers to predict the clinical fate of a patient before the presence of any symptom? In this seminar I will introduce the fundamentals of the theory of Dynamical Biomarkers of Networks (DBN), illustrating their application to the analysis of transcriptomic data during disease progression in a pathosystem model.
02 Juliol (16h) - Alex Arenas, DEIM, Universitat Rovira i Virgili
“Epidemics and mobility”
Abstract: Reaction–diffusion processes have been widely used to study epidemics in networked metapopulations. In the context of epidemics, reaction processes are understood as contagions within each subpopulation (patch), while diffusion represents the mobility of individuals between patches. Recently, the characteristics of human mobility, such as its recurrent nature, have been proven crucial to understand the phase transition to endemic epidemic states. Here, we present a framework able to cope with the elementary epidemic processes, the spatial distribution of populations and the commuting mobility patterns. We will show after, how this framework has been adapted to describe the COVID-19 pandemic.
16 Juliol (16h) - Ernesto Estrada, IUMA
“Fractional difusion on the human proteome as an alternative explanation to the multi-organ damage of SARS-CoV-2”
Abstract: SARS CoV-2 is the new coronavirus causing the pandemic known as COVID-19. This respiratory disease is characterized by multi-organ and systemic damages in patients. The abundance of ACE2 on human organs has been claimed as responsible for such multi-organ spread of the virus damages. However, once on circulation the virus could spread to practically every organ in the human body as ACE2 is ubiquitous on endothelia and smooth muscle cells of virtually all organs. Contrastingly, SARS CoV-2 only damages selectively a few organs. Here, we develop the hypothesis that the effects of the SARS CoV-2 virus can be spread through the human protein-protein interaction (PPI) network in a subdiffusive way. We then elaborate a time-fractional diffusion model on networks which allow us to study this phenomenon. Starting the diffusion from the SARS CoV-2 Spike protein to the human PPI network we show here that the pertubations can spread across the whole network in very few steps. Consequently, we discover a few potential routes of propagation of these perturbations from proteins mainly expressed in the lungs to proteins mainly expressed in other different tissues, such as the heart, cerebral cortex, thymus, lymph node, testis, prostate, liver, small intestine, duodenum, kidney, among others already reported as damaged by COVID-19.
Aneta Stefanovska (Lancaster University, UK)
In the real world, any system under study is subject to external perturbations. Mostly, these are either neglected, taken as part of the system, or treated as a noise. In this talk, we propose a fourth approach, which is to treat the system under study as being nonautonomous. We consider the particular case where there are perturbations to the phase of an oscillatory system. We will discuss the stability properties of such coupled systems and networks, and present a set of algorithms, from our toolbox MODA, that can be used for extracting their finite-time dynamical properties. The approach will be illustrated by applications to living systems including cells and the brain, as well as to physical systems such as rogue waves and electrons on the surface of superfluid helium.